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Relevant bibliographies by topics / Inferenza statistica

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Academic literature on the topic 'Inferenza statistica'

Author: Grafiati

Published: 9 March 2023

Last updated: 19 July 2025

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Journal articles on the topic "Inferenza statistica"

1

Ludbrook, John, and Hugh Dudley. "ISSUES IN BIOMEDICAL STATISTICS: STATISTICAL INFERENCE." ANZ Journal of Surgery 64, no. 9 (1994): 630–36. http://dx.doi.org/10.1111/j.1445-2197.1994.tb02308.x.

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Loosmore, N. Bert, and E. David Ford. "STATISTICAL INFERENCE USING THEGORKPOINT PATTERN SPATIAL STATISTICS." Ecology 87, no. 8 (2006): 1925–31. http://dx.doi.org/10.1890/0012-9658(2006)87[1925:siutgo]2.0.co;2.

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Barber, Stuart. "All of Statistics: a Concise Course in Statistical Inference." Journal of the Royal Statistical Society: Series A (Statistics in Society) 168, no. 1 (2005): 261. http://dx.doi.org/10.1111/j.1467-985x.2004.00347_18.x.

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Craigmile, Peter F. "All of Statistics: A Concise Course in Statistical Inference." American Statistician 59, no. 2 (2005): 203–4. http://dx.doi.org/10.1198/tas.2005.s30.

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Zhang, Jingwen, Joseph Ibrahim, Tengfei Li, and Hongtu Zhu. "A Powerful Global Test Statistic for Functional Statistical Inference." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5765–72. http://dx.doi.org/10.1609/aaai.v33i01.33015765.

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We consider the problem of performing an association test between functional data and scalar variables in a varying coefficient model setting. We propose a functional projection regression model and an associated global test statistic to aggregate relatively weak signals across the domain of functional data, while reducing the dimension. An optimal functional projection direction is selected to maximize signal-to-noise ratio with ridge penalty. Theoretically, we systematically study the asymptotic distribution of the global test statistic and provide a strategy to adaptively select the optimal

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Curran-Everett, Douglas. "Explorations in statistics: the bootstrap." Advances in Physiology Education 33, no. 4 (2009): 286–92. http://dx.doi.org/10.1152/advan.00062.2009.

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Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the sample mean. The appeal of the bootstrap is that we can use it to make an inference about some experimental result when the statistical theory is uncertain or even unknown. We can also use the bootstrap to assess how well the statistical theory holds: that

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Xu, Jinfeng, Lincheng Zhao, and Chenlei Leng. "Statistical inference for induced L-statistics: a random perturbation approach." Journal of Nonparametric Statistics 21, no. 7 (2009): 863–76. http://dx.doi.org/10.1080/10485250902980584.

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Subba Rao, Suhasini. "Statistical inference for spatial statistics defined in the Fourier domain." Annals of Statistics 46, no. 2 (2018): 469–99. http://dx.doi.org/10.1214/17-aos1556.

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Rohana, Rohana, and Yunika Lestaria Ningsih. "STUDENTS’ STATISTICAL REASONING IN STATISTICS METHOD COURSE." Jurnal Pendidikan Matematika 14, no. 1 (2019): 81–90. http://dx.doi.org/10.22342/jpm.14.1.6732.81-90.

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The role of statistics is wide and crucial in daily life, making statistics important. Many students have difficulty understanding statistics. This study aims to determine students' statistical reasoning about inference statistics, which is limited to the subject matter of the testing hypotheses about two-sample hypotheses testing. This study used descriptive research method. The subjects were 25 students of third-year Mathematics Education Departement at Universitas PGRI Palembang in the academic year 2018/2019. Data were collected through tests and interviews. Data were analyzed through desc

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SARASSANTI, YUMI, ALI HASMY, and YULIA RAHMAWATI Z. "PERSEPSI MAHASISWA TERHADAP KUIS PADA MATERI STATISTIKA INFERENSIA." AL KHAWARIZMI: Jurnal Pendidikan Matematika 3, no. 1 (2023): 19–22. https://doi.org/10.46368/kjpm.v3i1.1354.

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Abstrak: Tujuan Penelitian ini adalah untuk mengetahui persepsi mahasiswa terhadap kuis pada materi statistika inferensia. Penelitian ini menggunakan penelitian kualitatif deskriptif dengan metode penelitian survey, sampel penelitian ini diambil dari mahasiswa semester IV Program Studi Manajemen Dakwah IAIN Pontianak dengan Kelas A sebanyak 16 org dan kelas B sebanyak 20 orang. Teknik pengambilan data yang digunakan adalah angket. Berdasarkan hasil penelitian didapatkan hasil persepsi mahasiswa pada materi statistika inferensia secara garis besar termasuk berkategori baik dengan persentase 63,

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Dissertations / Theses on the topic "Inferenza statistica"

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AGOSTINELLI, Claudio. "Inferenza statistica robusta basata sulla funzione di verosimiglianza pesata: alcuni sviluppi." Doctoral thesis, country:ITA, 1998. http://hdl.handle.net/10278/25831.

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Capriati, Paola Bianca Martina. "L'utilizzo del metodo Bootstrap nella statistica inferenziale." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8715/.

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In questo lavoro viene introdotto il metodo Bootstrap, sviluppato a partire dal 1979 da Bradley Efron. Il Bootstrap è una tecnica statistica di ricampionamento basata su calcoli informatici, e quindi definita anche computer-intensive. In particolare vengono analizzati i vantaggi e gli svantaggi di tale metodo tramite esempi con set di dati reali implementati tramite il software statistico R. Tali analisi vertono su due tra i principali utilizzi del Bootstrap, la stima puntuale e la costruzione di intervalli di confidenza, basati entrambi sulla possibilità di approssimare la distribuzione campi

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Monda, Anna. "Inferenza non parametrica nel contesto di dati dipendenti: polinomi vocali e verosimiglianza empirica." Doctoral thesis, Universita degli studi di Salerno, 2013. http://hdl.handle.net/10556/1285.

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2010 - 2011<br>Il presente lavoro si inserisce nel contesto delle più recenti ricerche sugli strumenti di analisi non parametrica ed in particolare analizza l'utilizzo dei Polinomi Locali e della Verosimiglianza Empirica, nel caso di dati dipendenti. Le principali forme di dipendenza che verranno trattate in questo lavoro sono quelle che rispondono alla definizione di alpha-mixing ed in particolare il nostro si presenta come un tentativo di conciliare, in questo ambito, tecniche non parametriche, rappresentate dai Polinomi Locali, all'approccio di Empirical Likelihood, cercando di aggre

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Mancini, Martina. "Teorema di Cochran e applicazioni." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9145/.

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La statistica è un ramo della matematica che studia i metodi per raccogliere, organizzare e analizzare un insieme di dati numerici, la cui variazione è influenzata da cause diverse, con lo scopo sia di descrivere le caratteristiche del fenomeno a cui i dati si riferiscono, sia di dedurre, ove possibile, le leggi generali che lo regolano. La statistica si suddivide in statistica descrittiva o deduttiva e in statistica induttiva o inferenza statistica. Noi ci occuperemo di approfondire la seconda, nella quale si studiano le condizioni per cui le conclusioni dedotte dall'analisi statistica di un

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HAMMAD, AHMED TAREK. "Tecniche di valutazione degli effetti dei Programmi e delle Politiche Pubbliche. L' approccio di apprendimento automatico causale." Doctoral thesis, Università Cattolica del Sacro Cuore, 2022. http://hdl.handle.net/10280/110705.

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L'analisi dei meccanismi causali è stata considerata in varie discipline come la sociologia, l’epidemiologia, le scienze politiche, la psicologia e l’economia. Questi approcci permettere di scoprire relazioni e meccanismi causali studiando il ruolo di una variabile di trattamento (come ad esempio una politica pubblica o un programma) su un insieme di variabili risultato di interesse o diverse variabili intermedie sul percorso causale tra il trattamento e le variabili risultato. Questa tesi si concentra innanzitutto sulla revisione e l'esplorazione di strategie alternative per indagare gli eff

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BOLZONI, MATTIA. "Variational inference and semi-parametric methods for time-series probabilistic forecasting." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/313704.

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Prevedere la probabilità di eventi futuri è un problema comune. L'approccio più utilizzato assume una struttura fissa per questa probabilità, detta modello, dipendente da variabili latenti dette parametri. Dopo aver osservato dei dati è possibile inferire una distribuzione per queste variabili non osservabili. Il procedimento di inferenza non è sempre immediato, siccome selezionare un singolo valore per i parametri potrebbe portare a scarsi risultati, mentre approssimare una distribuzione usando MCMC potrebbe essere complicato. L'inferenza variazionale (VI) sta ricevendo una crescente attenzi

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MASPERO, DAVIDE. "Computational strategies to dissect the heterogeneity of multicellular systems via multiscale modelling and omics data analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/368331.

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L'eterogeneità pervade i sistemi biologici e si manifesta in differenze strutturali e funzionali osservate sia tra diversi individui di uno stesso gruppo (es. organismi o patologie), sia fra gli elementi costituenti di un singolo individuo (es. cellule). Lo studio dell’eterogeneità dei sistemi biologici e, in particolare, di quelli multicellulari è fondamentale per la comprensione meccanicistica di fenomeni fisiologici e patologici complessi (es. il cancro), così come per la definizione di strategie prognostiche, diagnostiche e terapeutiche efficaci. Questo lavoro è focalizzato sullo svilupp

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ROMIO, SILVANA ANTONIETTA. "Modelli marginali strutturali per lo studio dell'effetto causale di fattori di rischio in presenza di confondenti tempo dipendenti." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/8048.

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Uno degli obiettivi piu importanti della ricerca epidemiologica è quello di analizzare la relazione tra uno o più fattori di rischio ed un evento. Tali relazioni sono spesso complicate dalla presenza di confondenti, il cui concetto è estremamente complesso da formalizzare. Dal punto di vista dell'analisi causale, si dice che esiste confondimento quando la misura di associazione non coincide con quella di effetto corrispondente, cioè quando ad esempio il rischio relativo non coincide con il rischio relativo causale. Il problema è quindi quello di individuare i disegni e le ipotesi sulla base d

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Zeller, Camila Borelli. "Modelo de Grubbs em grupos." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307093.

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Orientador: Filidor Edilfonso Vilca Labra<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica<br>Made available in DSpace on 2018-08-05T23:55:16Z (GMT). No. of bitstreams: 1 Zeller_CamilaBorelli_M.pdf: 3683998 bytes, checksum: 26267086098b12bd76b1d5069f688223 (MD5) Previous issue date: 2006<br>Resumo: Neste trabalho, apresentamos um estudo de inferência estatística no modelo de Grubbs em grupos, que representa uma extensão do modelo proposto por Grubbs (1948,1973) que é freqüentemente usado para comparar instrumentos ou

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Filiasi, Mario. "Applications of Large Deviations Theory and Statistical Inference to Financial Time Series." Doctoral thesis, Università degli studi di Trieste, 2015. http://hdl.handle.net/10077/10940.

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2013/2014<br>La corretta valutazione del rischio finanziario è una delle maggiori attività nell'amibto della ricerca finanziaria, ed è divenuta ancora più importante dopo la recente crisi finanziaria. I recenti progressi dell'econofisica hanno dimostrato come la dinamica dei mercati finanziari può essere studiata in modo attendibile per mezzo dei modelli usati in fisica statistica. L'andamento dei prezzi azionari è costantemente monitorato e registrato ad alte frequenze (fino a 1ms) e ciò produce un'enorme quantità di dati che può essere analizzata statisticamente per validare e calibrare i mo

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Books on the topic "Inferenza statistica"

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Mukhopadhyay, Nitis. Probability and statistical inference. Marcel Dekker, 2000.

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S, Maddala G., and Rao C. R, eds. Robust inference. Elsevier, 1997.

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Bromek, Tadeusz, and Elżbieta Pleszczyńska, eds. Statistical Inference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0575-7.

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Panik, Michael J. Statistical Inference. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118309773.

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L, Berger Roger, ed. Statistical inference. Brooks/Cole Pub. Co., 1990.

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Silvey, S. D. Statistical inference. Chapman and Hall, 1988.

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Garthwaite, Paul H. Statistical inference. Prentice Hall, 1995.

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Garthwaite, Paul H. Statistical inference. 2nd ed. Oxford University Press, 2002.

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Oakes, Michael W. Statistical inference. Epidemiology Resources Inc., 1990.

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L, Berger Roger, ed. Statistical Inference. 2nd ed. Thomson Learning, 2002.

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Book chapters on the topic "Inferenza statistica"

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Rotondi, Alberto, Paolo Pedroni, and Antonio Pievatolo. "Inferenza statistica e verosimiglianza." In UNITEXT. Springer Milan, 2005. http://dx.doi.org/10.1007/88-470-0348-2_9.

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Rotondi, Alberto, Paolo Pedroni, and Antonio Pievatolo. "Inferenza statistica e verosimiglianza." In UNITEXT. Springer Milan, 2021. http://dx.doi.org/10.1007/978-88-470-4010-6_10.

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Rotondi, Alberto, Paolo Pedroni, and Antonio Pievatolo. "Inferenza statistica e verosimiglianza." In UNITEXT. Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2364-2_9.

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Battaglia, Francesco. "Inferenza statistica per processi aleatori." In UNITEXT. Springer Milan, 2007. http://dx.doi.org/10.1007/978-88-470-0603-4_8.

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Nordholt, Eric Schulte. "Applications of Statistical Disclosure Control at Statistics Netherlands." In Inference Control in Statistical Databases. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47804-3_17.

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Ewens, Warren J., and Gregory R. Grant. "Statistics (i): An Introduction to Statistical Inference." In Statistical Methods in Bioinformatics. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3247-4_3.

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Lynch, Scott M. "Statistical Inference." In Using Statistics in Social Research. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8573-5_6.

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Kroese, Dirk P., and Joshua C. C. Chan. "Statistical Inference." In Statistical Modeling and Computation. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8775-3_5.

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Rahlf, Thomas. "Statistical Inference." In Handbook of Cliometrics. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-40406-1_16.

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Gooch, Jan W. "Statistical Inference." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15388.

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Conference papers on the topic "Inferenza statistica"

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Martínez-Rodríguez, Ana María, Antonia Oya, Raquel Caballero-Águila, Rosa Fernández-Alcalá, María del Pilar Frías-Bustamante, and María José Olmo-Jiménez. "GAMIFICATION IN TEACHING STATISTICAL INFERENCE." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.1013.

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Jones, Peter, Kay Lipson, and Brian Phillips. "A role for computer intensive methods in introducing statistical inference." In Proceedings of the First Scientific Meeting of the IASE. International Association for Statistical Education, 1993. http://dx.doi.org/10.52041/srap.93311.

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Introductory statistics courses have become increasingly prevalent in a wide variety of undergraduate and graduate programs over the past few years. This has resulted in the study of inferential statistics becoming the norm rather than exception. Whilst statistics was once a course chosen by the more mathematically able student, many current students of statistics have little mathematical aptitude or expertise (Tanis, 1992 for example). As a result, many introductory statistic courses have moved away from including much of the statistical theory that underpins inference to become basically tec

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Borovcnik, Manfred. "Informal inference – approaches towards statistical inference." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19101.

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The development of methods suitable to tackle the problem of inductive logic – how to justify arguments that generalise findings from data – has been signified by great controversies in the foundations and – later – also in statistics education. There have been several attempts to reconcile the various approaches or to simplify statistical inference: EDA, Non-parametric statistics, and the Bootstrap. EDA focuses on a strong connection between data and context, non parametrics reduces the complexity of the model, and Bootstrap rests solely on the data. Informal inference subsumes two different

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Weldon, Larry. "From data to graphs to words - but where are the models?" In Statistics Education and the Communication of Statistics. International Association for Statistical Education, 2005. http://dx.doi.org/10.52041/srap.05206.

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The pioneers of statistics focused on parametric estimation and summary to communicate statistical findings. The tradition of basing inference on parametric fits is a central mode in statistics education, but in statistics applications, computer- based graphical summary is playing an increasingly important role. A parallel development has been the spread of statistics education to almost all disciplines, and thus the need to communicate statistical results to non-specialists has become more acute. These influences of more graphics and a wider distribution require adaptation in our statistics c

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García-García, Jamie, Gonzalo Chávez, Liliana Tauber, and Nicolás Fernández. "Knowledge Elements of Statistical Literacy in Informal Inferential Reasoning of Middle School Students." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t14f1.

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This study evaluates the knowledge elements of statistical literacy (literacy skills, statistical knowledge, mathematical knowledge, context knowledge, and critical skills) mobilized by thirty students of secondary education, against questions of informal statistical inference. Based on the SOLO taxonomy, four hierarchical levels (prestructural, unistructural, multistructural, and relational) are defined to analyze the responses. The study shows that ‘critical skills’ and ‘statistical knowledge’ are the elements that move students most often, classified as unistructural, because they perform a

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Mu, Weiyan, and Xiaona Yuan. "Statistical inference for ANOVA under heteroscedasticity: Statistical inference." In 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2012. http://dx.doi.org/10.1109/cecnet.2012.6201745.

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Liu, Wenqi, and Von Bing Yap. "On inferential techniques used in studies on teaching statistics." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19421.

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Amidst the current debate on the practice of statistical inference, more attention should be directed to the random-sampling assumption. We demonstrate the ubiquity of the assumption in sample surveys, controlled experiments and regression models, and argue that the assumption is crucial to inference. If it fails, as is often the case, not only the P value, but also the confidence interval may not be meaningful. An informal survey of papers that present include inferential data analysis found a low rate of a mention of the issue: 1 in 10 papers in two volumes of Journal of Statistics Education

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Ocampo, Shirlee, and Bladimir Ocampo. "Capacity building through project based learning in Bayesian statistics." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19411.

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Outcome-based education requires statistics education in the 21st century to be structured holistically by allowing the students to work with real life data along with visualization, computation, and learning outputs. The new K-12 curriculum resulted to a chain reaction in the Statistics undergraduate program by including alternative statistical frameworks such as Bayesian statistics. This paper focuses on project-based learning approach on designing learning outputs for undergraduate and graduate students in Bayesian statistics. The stages of project-based learning in completing these Bayesia

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Mézard, Marc. "Statistical physics and statistical inference." In GECCO '21: Genetic and Evolutionary Computation Conference. ACM, 2021. http://dx.doi.org/10.1145/3449639.3465420.

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Vancsó, Ödön, Peter Fejes Tóth, and Manfred Borovcnik. "Conditional Probability, Bayes and Classical Statistics—Evaluation of the Planned Secondary-School Reform in Hungary." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t6g2.

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Our main goal is to show that we can use classical and Bayesian statistics, including theoretical background, for introducing statistical inference in Hungarian high schools. In a first step, we will test the material we are developing with higher-level students. We introduce the concept of the project, including its theoretical background and previous pilot teaching experiments. The basis is the parallel introduction of classical and Bayesian methods of statistical inference. Long-term experience from research work and from seminars with future teachers provide evidence that this parallel app

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Reports on the topic "Inferenza statistica"

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Tintle, Nathan, Beth Chance, Laura Callis, and Jennifer McNally. Teaching Statistics via Active Investigations (Free Seminar). Instats Inc., 2025. https://doi.org/10.61700/chnzshtl0emoe1704.

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This comprehensive seminar, led by members of the NSF-funded Exploring the Art and Practice of Statistics Teaching (EAPOST) team, equips educators with innovative approaches for teaching introductory statistics, focusing on active learning, the statistical investigation process, and simulation-based inference. Participants will gain practical tools to enhance student engagement and conceptual understanding in the classes they teach, with insights tailored to diverse educational settings.

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Carroll, Raymond J. Research in Statistical Inference. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada252928.

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Manski, Charles F. Remarks on statistical inference for statistical decisions. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.06.

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Manski, Charles F. Remarks on statistical inference for statistical decisions. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.0619.

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Martinez, Melissa. Hypothesis Testing. ConductScience, 2022. http://dx.doi.org/10.55157/cs20220615.

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Hypothesis testing is a statistical method used to evaluate the validity of a potential outcome within a defined significance level, comparing it with an alternative hypothesis. It involves establishing null and alternative hypotheses, making assumptions, calculating a test statistic, and selecting a significance level. The decision to accept or reject the null hypothesis is based on the observed test statistic. Terminologies include null and alternative hypotheses, critical region, critical value, errors, p-value, power of a test, and more. Hypothesis testing is crucial in statistical inferen

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David, Robert A. Order Statistics and Robust Inference. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203440.

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Karr, Alan F. Statistical Inference for Stochastic Processes. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada190491.

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Masry, Elias. Statistical Inference from Sampled Data. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada342544.

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Gimpel, K., and D. Rudoy. Statistical Inference in Graphical Models. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada482530.

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Batchelder, William H. Statistical Inference for Cultural Consensus Theory. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada605989.

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